The growth of the telemarketing industry in recent years has been phenomenal. More and more companies are employing cold-calling techniques to solicit sales. This is especially true with the growing prevalence of lower carrier rates and the availability of customer lists via dedicated list providers.
With any cold-calling mechanism, efficiency is paramount. Contact centers strive for a high throughput of calls in the expectation of sales. Outbound diallers for contact centers have evolved in recent years from manual based preview diallers to more advanced predictive dialling systems. Basic outbound applications offer a contact to an agent allowing them to manually dial customers or offering them the option to cause the system to automate the dialling task on behalf of the agent. This preview or progressive method is inefficient for many reasons: the agent's time is spent waiting for customers to answer; the agent must listen to tones to determine how an unsuccessful dialling attempt should be characterised and recorded i.e. “Ring No Answer”, “Busy”, “Wrong Number” etc. Because the agent is in control of the timing of the call and its release, there can be performance issues.
More modern progressive diallers are implemented in many large contact centers. In these contact centers, a dialler application calls the customer and has the capability of detecting the progress of the call attempt without human input. In the event of a live customer answering the call, it gets offered to an agent, thus eliminating the offering of failed calls to agents. These systems contain a “Predictive Algorithm” which forecasts when an agent is likely to become available to take a call and tries to have a live customer reached as an agent becomes available. In this way, the dialler can pace outbound call initiation attempts accordingly to maximise the talk-time of the agent.
Predictive algorithms use a combination of historical information and current agent talktime as well as failed call rates to “predict” how frequently calls need to be made to eliminate agent idle time. Inevitably however, some algorithms either dial customers too frequently meaning there are no agents available (“abandoned calls”) to take the call or too infrequently leaving agents idle. Regional regulations define the permitted abandoned call rates on outbound campaigns and impose severe penalties for breaches. It is therefore crucial that the algorithm paces calls within the regulatory tolerances.
Current predictive algorithms retain historical information about campaigns and adjust to the current agent talk-time. For example, if a predictive outbound dialling campaign targeted retired people in a given area for telemarketing sales of home insurance and found that the average talk time of an agent was 2.5 minutes per call, it would infer that future calls of the same campaign would take the same time. This mechanism however does not take account of calls which have a higher variance from average talk-time. A short call could occur when a prospective customer is not interested, while a longer call could occur when there is interest or a quotation is being prepared. Other factors such as average wrap-up time are also used today by predictive algorithms, again based on historical information.